Open AccessUnraveling schizophrenia replicable functional connectivity disruption patterns across sites
Author(s) -
Du Xiaotong,
Wei Xiaotong,
Ding Hao,
Yu Ying,
Xie Yingying,
Ji Yi,
Zhang Yu,
Chai Chao,
Liang Meng,
Li Jie,
Zhuo Chuanjun,
Yu Chunshui,
Qin Wen
Publication year - 2023
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.26108
Subject(s) - neuroscience , subnet , schizophrenia (object oriented programming) , receiver operating characteristic , thalamus , set (abstract data type) , pattern recognition (psychology) , psychology , artificial intelligence , computer science , machine learning , psychiatry , computer network , programming language
Functional connectivity (FC) disruption is a remarkable characteristic of schizophrenia. However, heterogeneous patterns reported across sites severely hindered its clinical generalization. Based on qualified nodal‐based FC of 340 schizophrenia patients (SZ) and 348 normal controls (NC) acquired from seven different scanners, this study compared four commonly used site‐effect correction methods in removing the site‐related heterogeneities, and then tried to cluster the abnormal FCs into several replicable and independent disrupted subnets across sites, related them to clinical symptoms, and evaluated their potentials in schizophrenia classification. Among the four site‐related heterogeneity correction methods, ComBat harmonization (F1 score: 0.806 ± 0.145) achieved the overall best balance between sensitivity and false discovery rate in unraveling the aberrant FCs of schizophrenia in the local and public data sets. Hierarchical clustering analysis identified three replicable FC disruption subnets across the local and public data sets: hypo‐connectivity within sensory areas (Net1), hypo‐connectivity within thalamus, striatum, and ventral attention network (Net2), and hyper‐connectivity between thalamus and sensory processing system (Net3). Notably, the derived composite FC within Net1 was negatively correlated with hostility and disorientation in the public validation set ( p < .05). Finally, the three subnet‐specific composite FCs (Best area under the receiver operating characteristic curve [AUC] = 0.728) can robustly and meaningfully discriminate the SZ from NC with comparable performance with the full identified FCs features (best AUC = 0.765) in the out‐of‐sample public data set ( Z = −1.583, p = .114). In conclusion, ComBat harmonization was most robust in detecting aberrant connectivity for schizophrenia. Besides, the three subnet‐specific composite FC measures might be replicable neuroimaging markers for schizophrenia.